Fortunately, researchers have sought to develop automatic and data-driven approaches for the scalable discovery of differentiated patterns of care.
Particularly, machine learning techniques in the anomalous pattern detection literature could be employed to encode variations in healthcare.
Subset scanning  [1]}, [2]} is one of these approaches that aim to identify anomalous subgroups in a population with divergent characteristics compared to the expected baseline in an efficient manner. In subset scanning, anomalousness is quantified by a scoring function, typically a log-likelihood ratio statistic, that is maximized over the subsets in a multidimensional array to identify the subset with the highest score  [1]}. This scoring function must satisfy the Linear-Time Subset Scanning (LTSS) so that the search is efficiently conducted in linear time [1]}.
